



import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 		lowerCAmelCase__     (       UpperCamelCase_					:							bytes			,							UpperCamelCase_					:							int						)->	int:
    A__					=       f"{sampling_rate}"
    A__					=       '''1'''
    A__					=       '''f32le'''
    A__					=       [
        '''ffmpeg''',
        '''-i''',
        '''pipe:0''',
        '''-ac''',
        ac,
        '''-ar''',
        ar,
        '''-f''',
        format_for_conversion,
        '''-hide_banner''',
        '''-loglevel''',
        '''quiet''',
        '''pipe:1''',
    ]

    try:
        with subprocess.Popen(a_			,							stdin=subprocess.PIPE			,							stdout=subprocess.PIPE						) as ffmpeg_process:
            A__					=       ffmpeg_process.communicate(a_						)
    except FileNotFoundError as error:
        raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''						) from error
    A__					=       output_stream[0]
    A__					=       np.frombuffer(a_			,							np.floataa						)
    if audio.shape[0] == 0:
        raise ValueError('''Malformed soundfile'''						)
    return audio



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int			,							UpperCamelCase_					:							float			,							UpperCamelCase_					:							str = "f32le"			,							)->	Dict:
    A__					=       f"{sampling_rate}"
    A__					=       '''1'''
    if format_for_conversion == "s16le":
        A__					=       2
    elif format_for_conversion == "f32le":
        A__					=       4
    else:
        raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"						)

    A__					=       platform.system()
    if system == "Linux":
        A__					=       '''alsa'''
        A__					=       '''default'''
    elif system == "Darwin":
        A__					=       '''avfoundation'''
        A__					=       ''':0'''
    elif system == "Windows":
        A__					=       '''dshow'''
        A__					=       '''default'''

    A__					=       [
        '''ffmpeg''',
        '''-f''',
        format_,
        '''-i''',
        input_,
        '''-ac''',
        ac,
        '''-ar''',
        ar,
        '''-f''',
        format_for_conversion,
        '''-fflags''',
        '''nobuffer''',
        '''-hide_banner''',
        '''-loglevel''',
        '''quiet''',
        '''pipe:1''',
    ]
    A__					=       int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
    A__					=       _ffmpeg_stream(a_			,							a_						)
    for item in iterator:
        yield item



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int			,							UpperCamelCase_					:							float			,							UpperCamelCase_					:							Optional[int] = None			,							UpperCamelCase_					:							Optional[Union[Tuple[float, float], float]] = None			,							UpperCamelCase_					:							str = "f32le"			,							)->	List[Any]:
    if stream_chunk_s is not None:
        A__					=       stream_chunk_s
    else:
        A__					=       chunk_length_s

    A__					=       ffmpeg_microphone(a_			,							a_			,							format_for_conversion=a_						)
    if format_for_conversion == "s16le":
        A__					=       np.intaa
        A__					=       2
    elif format_for_conversion == "f32le":
        A__					=       np.floataa
        A__					=       4
    else:
        raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"						)

    if stride_length_s is None:
        A__					=       chunk_length_s / 6
    A__					=       int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
    if isinstance(a_			,							(int, float)						):
        A__					=       [stride_length_s, stride_length_s]

    A__					=       int(round(sampling_rate * stride_length_s[0]						)						) * size_of_sample
    A__					=       int(round(sampling_rate * stride_length_s[1]						)						) * size_of_sample
    A__					=       datetime.datetime.now()
    A__					=       datetime.timedelta(seconds=a_						)
    for item in chunk_bytes_iter(a_			,							a_			,							stride=(stride_left, stride_right)			,							stream=a_						):
        # Put everything back in numpy scale
        A__					=       np.frombuffer(item['''raw''']			,							dtype=a_						)
        A__					=       (
            item['''stride'''][0] // size_of_sample,
            item['''stride'''][1] // size_of_sample,
        )
        A__					=       sampling_rate
        audio_time += delta
        if datetime.datetime.now() > audio_time + 1_0 * delta:
            # We're late !! SKIP
            continue
        yield item



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Dict			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							Tuple[int, int]			,							UpperCamelCase_					:							bool = False						)->	Optional[Any]:
    A__					=       b''''''
    A__			,						A__					=       stride
    if stride_left + stride_right >= chunk_len:
        raise ValueError(
            f"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"						)
    A__					=       0
    for raw in iterator:
        acc += raw
        if stream and len(a_						) < chunk_len:
            A__					=       (_stride_left, 0)
            yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
        else:
            while len(a_						) >= chunk_len:
                # We are flushing the accumulator
                A__					=       (_stride_left, stride_right)
                A__					=       {'''raw''': acc[:chunk_len], '''stride''': stride}
                if stream:
                    A__					=       False
                yield item
                A__					=       stride_left
                A__					=       acc[chunk_len - stride_left - stride_right :]
        # Last chunk
    if len(a_						) > stride_left:
        A__					=       {'''raw''': acc, '''stride''': (_stride_left, 0)}
        if stream:
            A__					=       False
        yield item



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str			,							UpperCamelCase_					:							int						)->	Optional[Any]:
    A__					=       2**2_4  # 16Mo
    try:
        with subprocess.Popen(a_			,							stdout=subprocess.PIPE			,							bufsize=a_						) as ffmpeg_process:
            while True:
                A__					=       ffmpeg_process.stdout.read(a_						)
                if raw == b"":
                    break
                yield raw
    except FileNotFoundError as error:
        raise ValueError('''ffmpeg was not found but is required to stream audio files from filename'''						) from error






'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate





@dataclass(frozen=__magic_name__			)
class  __lowercase    (	__magic_name__			):
   _a    						=   field(default="""audio-classification"""							,				metadata={"""include_in_asdict_even_if_is_default""": True}			)
   _a    						=   Features({"""audio""": Audio()}			)
   _a    						=   Features({"""labels""": ClassLabel}			)
   _a    						=   "audio"
   _a    						=   "labels"







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     if self.label_column not in features:
       raise ValueError(f"Column {self.label_column} is not present in features."   )
     if not isinstance(features[self.label_column]   ,				UpperCamelCase   ):
       raise ValueError(f"Column {self.label_column} is not a ClassLabel."   )
     __a								=       copy.deepcopy(self   )
     __a								=       self.label_schema.copy()
     __a								=       features[self.label_column]
     __a								=       label_schema
     return task_template







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict[str, str]:
     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }



